Light-Fueled Liquid Crystal Networks for Aquatic Soft Robotics
Notice bibliographique
Résumé
The field of soft robotics has developed in response to the need for mechanisms that can operate safely in interaction with humans. Within the field of soft robotics, there is a growing demand for the development of small-scale soft devices capable of non-invasive medical interventions in a variety of technologies, including medical microrobotics, biosensing, and biomedical engineering. Among the many materials used for small-scale soft robotics, liquid crystal networks (LCNs) are of particular interest. LCNs are molecularly anisotropic and demonstrate reversible, programmable shape change upon exposure to external stimuli such as light or heat. However, the actuation of LCNs is often triggered by light, either photochemically or photothermally, which is typically less efficient when operating in flooded environments. Actuation in confined and flooded environments is an important challenge that must be overcome for the implementation of LCNs in real-world biomedical applications. Decoupling the mechanisms of powering, locomotion, and control from robotic functions is a strong solution for achieving efficient operation in flooded media. This work showcases two examples of decoupling locomotion and robotic function for the efficient use of small-scale devices at the air-water interface. This is done through the use of LCNs for control in conjunction with protein motors for powering. An LCN that responds to visible light, rather than UV, is also fabricated and characterized, and will be used for underwater robotic applications in future studies. \nIn the first case study, protein motors and LCNs are used to power and control a multi-component mechanical device. A milli-scale gear train with integrated motor and clutch functionalities is fabricated and operated at the air-water interface. The driving gear has a protein motor coating and generates propulsive force using the Marangoni effect. This force is transmitted through the gear train unless the clutch gear is activated. The clutch gear is fabricated from a photothermally responsive LCN that has been plasticized through the addition of a nematogenic solvent to improve actuation efficiency. The teeth of the clutch gear bend downwards to disengage from the gear train upon exposure to light, halting the chain of motion on demand. The second case study utilizes photochemical LCNs with applied protein motor coating to construct a V-shaped swimmer that moves across the surface of the water. Photochemical LCNs are used for their meta-stable state that allows for deformations to be held without constant exposure to stimuli. The protein motors are integrated directly onto the LCN swimmer for a single device with orthogonal mechanisms for powering and control. The protein motors generate force that propels the swimmer forward while deformation of the LCN is used for directional control. \nIn both case studies, UV light is required for shape-change. Looking forward to biomedical applications, a photochemical LCN that is responsive to visible light is also developed and characterized in both air and water. The design of LCN-based small-scale devices with a focus on safe and efficient operation in wet environments will open up new and exciting applications.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».